How to Use This Book

This textbook is designed for multiple uses and multiple audiences. Before you dive into Chapter 1, take five minutes to understand how the book is structured and how to navigate it most effectively for your purposes.

The Eight-Part Structure

The book is organized into eight thematic parts, each building on those before it:

  • Part I (Chapters 1–5): Foundations — Why do we believe what we believe? What makes a source trustworthy? How do cognitive biases and social dynamics make us vulnerable to misinformation? Start here if you're new to these questions.

  • Part II (Chapters 6–10): The Information Ecosystem — How did we get here? From the penny press to the attention economy, this part traces the structural conditions that make misinformation so pervasive today.

  • Part III (Chapters 11–18): Types and Mechanisms — What kinds of misinformation exist, and how does each work? This part is a field guide to the specific forms misinformation takes, from propaganda to deepfakes.

  • Part IV (Chapters 19–24): Detection and Analysis — How do we identify misinformation? Professional fact-checking, source evaluation, data literacy, and three Python-intensive chapters on computational detection methods.

  • Part V (Chapters 25–29): Critical Thinking Skills — The cognitive toolkit for resistance: logic, scientific thinking, probabilistic reasoning, lateral reading, and established media literacy frameworks.

  • Part VI (Chapters 30–33): Political Dimensions — Democracy, polarization, state-sponsored disinformation, election interference, and global policy responses.

  • Part VII (Chapters 34–38): Countermeasures — Platform content moderation, inoculation theory, education-based interventions, regulatory approaches, and building personal resilience.

  • Part VIII (Chapters 39–41): Advanced Topics — Generative AI, global perspectives, and the ethics of truth. Graduate-level extensions of the core material.

The Chapter Structure

Every chapter follows the same format:

File Contents
index.md Main chapter content (8,000–12,000 words)
exercises.md 25–40 problems (⭐ to ⭐⭐⭐⭐ difficulty)
quiz.md 20–30 self-assessment questions with hidden answers
case-study-01.md Primary case study with data and Python code
case-study-02.md Secondary case study
key-takeaways.md Chapter summary card — review this first or last
further-reading.md Annotated bibliography for deeper study
code/ Python examples and solutions

Our recommendation: Read key-takeaways.md before the chapter to orient yourself, then read the full chapter, then complete exercises before checking answers.

Icon Legend

Throughout the chapters, you'll encounter five types of callout boxes:

💡 Intuition: Mental models and analogies that build intuition before the formal treatment.

📊 Real-World Application: Concrete examples of how concepts play out in the world.

⚠️ Common Pitfall: The most frequent mistakes people make with this concept — and how to avoid them.

🎓 Advanced: Graduate-level extensions. These sections can be skipped on a first reading without losing the main thread.

✅ Best Practice: Research-backed recommendations for applying the concepts in practice.

Suggested Reading Paths

Linear (recommended for courses): Read all chapters in order. Each chapter assumes the previous ones.

Practitioner's Path (for working journalists, fact-checkers, or communicators): Parts I (Chapters 3–5), III (11–13), IV (19–21), V (25–27), VII (35, 38). Then add Chapters 39 and 41.

Policy Path (for government, advocacy, or legal work): Parts I (Chapters 1–2), II (full), VI (full), VII (34, 37), VIII (40–41).

Technical Path (for data scientists and engineers): Parts I (Chapters 3–4), III (11, 18), IV (full — especially 22–24), V (28), VIII (39). All code/ directories.

Crash Course (weekend reading, one chapter per session): Chapters 1, 4, 7, 8, 11, 13, 15, 19, 20, 22, 25, 30, 35, 38, 39, 41.

Approaching the Python Code

Chapters 22, 23, 24, and the technical examples throughout assume basic Python familiarity. If you're new to Python:

  1. Install Python 3.10+ and run pip install -r requirements.txt from the root directory
  2. See Appendix C for a library-by-library reference guide
  3. Each code file is self-contained and includes a __main__ block you can run directly
  4. All code follows PEP 8 and includes Google-style docstrings with usage examples

You do not need to understand every line of code in the technical chapters to get the conceptual value from them. Read the surrounding explanation, understand what the code is trying to do, and run it.

For Instructors

This book is designed for a semester-long course meeting twice weekly. One natural division: Parts I–IV in the first half of the semester, Parts V–VIII in the second. The three Capstone Projects in Part IX are suitable for end-of-semester projects.

Assessment suggestions: quizzes from each chapter's quiz.md file (auto-gradeable), exercises from exercises.md (gradeable with the provided rubrics), and one capstone project from Part IX.

Instructor supplements, including slide decks and full exercise solutions, are available at the publisher's website.